Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.

All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.

All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.

In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.

Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.

Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.

Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.